Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries.Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution.Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990-2010 time period, with the greatest annualised rate of decline occurring in the 0-9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10-24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the...

Summary Background Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950. Findings Globally, 18·7% (95% uncertainty interval 18·4–19·0) of deaths were registered in 1950 and that proportion has been steadily increasing since, with 58·8% (58·2–59·3) of all deaths being registered in 2015. At the global level, between 1950 and 2017, life expectancy increased from 48·1 years (46·5–49·6) to 70·5 years (70·1–70·8) for men and from 52·9 years (51·7–54·0) to 75·6 years (75·3–75·9) for women. Despite this overall progress, there remains substantial variation in life expectancy at birth in 2017, which ranges from 49·1 years (46·5–51·7) for men in the Central African Republic to 87·6 years (86·9–88·1) among women in Singapore. The greatest progress across age groups was for children younger than 5 years; under-5 mortality dropped from 216·0 deaths (196·3–238·1) per 1000 livebirths in 1950 to 38·9 deaths (35·6–42·83) per 1000 livebirths in 2017, with huge reductions acro...

Summary Background Since 2000, the scale-up of malaria control interventions has substantially reduced morbidity and mortality caused by the disease globally, fuelling bold aims for disease elimination. In tandem with increased availability of geospatially resolved data, malaria control programmes increasingly use high-resolution maps to characterise spatially heterogeneous patterns of disease risk and thus efficiently target areas of high burden. Methods We updated and refined the Plasmodium falciparum parasite rate and clinical incidence models for sub-Saharan Africa, which rely on cross-sectional survey data for parasite rate and intervention coverage. For malaria endemic countries outside of sub-Saharan Africa, we produced estimates of parasite rate and incidence by applying an ecological downscaling approach to malaria incidence data acquired via routine surveillance. Mortality estimates were derived by linking incidence to systematically derived vital registration and verbal autopsy data. Informed by high-resolution covariate surfaces, we estimated P falciparum parasite rate, clinical incidence, and mortality at national, subnational, and 5 × 5 km pixel scales with corresponding uncertainty metrics. Findings We present the first global, high-resolution map of P falciparum malaria mortality and the first global prevalence and incidence maps since 2010. These results are combined with those for Plasmodium vivax (published separately) to form the malaria estimates for the Global Burden of Disease 2017 study. The P falciparum estimates span the period 2000–17, and illustrate the rapid decline in burden between 2005 and 2017, with incidence declining by 27·9% and mortality declining by 42·5%. Despite a growing population in endemic regions, P falciparum cases declined between 2005 and 2017, from 232·3 million (95% uncertainty interval 198·8–277·7) to 193·9 million (156·6–240·2) and deaths declined from 925 800 (596 900–1 341 100) to 618 700 (368 600–952 200). Despite the declines in burden, 90·1% of people within sub-Saharan Africa continue to reside in endemic areas, and this region accounted for 79·4% of cases and 87·6% of deaths in 2017. Interpretation High-resolution maps of P falciparum provide a contemporary resource for informing global policy and malaria control planning, programme implementation, and monitoring initiatives. Amid progress in reducing global malaria burden, areas where incidence trends have plateaued or increased in the past 5 years underscore the fragility of hard-won gains against malaria. Efforts towards elimination should be strengthened in such areas, and those where burden remained high throughout the study period. Funding Bill & Melinda Gates Foundation. ...

Background Plasmodium vivax exacts a significant toll on health worldwide, yet few efforts to date have quantified the extent and temporal trends of its global distribution. Given the challenges associated with the proper diagnosis and treatment of P vivax, national malaria programmes-particularly those pursuing malaria elimination strategiesrequire up to date assessments of P vivax endemicity and disease impact. This study presents the first global maps of P vivax clinical burden from 2000 to 2017. Methods In this spatial and temporal modelling study, we adjusted routine malariometric surveillance data for known biases and used socioeconomic indicators to generate time series of the clinical burden of P vivax. These data informed Bayesian geospatial models, which produced fine-scale predictions of P vivax clinical incidence and infection prevalence over time. Within sub-Saharan Africa, where routine surveillance for P vivax is not standard practice, we combined predicted surfaces of Plasmodium falciparum with country-specific ratios of P vivax to P falciparum. These results were combined with surveillance-based outputs outside of Africa to generate global maps. Findings We present the first high-resolution maps of P vivax burden. These results are combined with those for P falciparum (published separately) to form the malaria estimates for the Global Burden of Disease 2017 study. The burden of P vivax malaria decreased by 41•6%, from 24•5 million cases (95% uncertainty interval 22•5-27•0) in 2000 to 14•3 million cases (13•7-15•0) in 2017. The Americas had a reduction of 56•8% (47•6-67•0) in total cases since 2000, while SouthEast Asia recorded declines of 50•5% (50•3-50•6) and the Western Pacific regions recorded declines of 51•3% (48•0-55•4). Europe achieved zero P vivax cases during the study period. Nonetheless, rates of decline have stalled in the past five years for many countries, with particular increases noted in regions affected by political and economic instability. Interpretation Our study highlights important spatial and temporal patterns in the clinical burden and prevalence of P vivax. Amid substantial progress worldwide, plateauing gains and areas of increased burden signal the potential for challenges that are greater than expected on the road to malaria elimination. These results support global monitoring systems and can inform the optimisation of diagnosis and treatment where P vivax has most impact. Funding Bill & Melinda Gates Foundation and the Wellcome Trust.

BackgroundThe Malaria Atlas Project (MAP) has worked to assemble and maintain a global open-access database of spatial malariometric data for over a decade. This data spans various formats and topics, including: geo-located surveys of malaria parasite rate; global administrative boundary shapefiles; and global and regional rasters representing the distribution of malaria and associated illnesses, blood disorders, and intervention coverage. MAP has recently released malariaAtlas, an R package providing a direct interface to MAP’s routinely-updated malariometric databases and research outputs.Methods and resultsThe current paper reviews the functionality available in malariaAtlas and highlights its utility for spatial epidemiological analysis of malaria. malariaAtlas enables users to freely download, visualise and analyse global malariometric data within R. Currently available data types include: malaria parasite rate and vector occurrence point data; subnational administrative boundary shapefiles; and a large suite of rasters covering a diverse range of metrics related to malaria research. malariaAtlas is here used in two mock analyses to illustrate how this data may be incorporated into a standard R workflow for spatial analysis.ConclusionsmalariaAtlas is the first open-access R-interface to malariometric data, providing a new and reproducible means of accessing such data within a freely available and commonly used statistical software environment. In this way, the malariaAtlas package aims to contribute to the environment of data-sharing within the malaria research community.Electronic supplementary materialThe online version of this article (10.1186/s12936-018-2500-5) contains supplementary material, which is available to authorized users.

Individual malaria infections can carry multiple strains of Plasmodium falciparum with varying levels of relatedness. Yet, how local epidemiology affects the properties of such mixed infections remains unclear. Here, we develop an enhanced method for strain deconvolution from genome sequencing data, which estimates the number of strains, their proportions, identity-by-descent (IBD) profiles and individual haplotypes. Applying it to the Pf3k data set, we find that the rate of mixed infection varies from 29% to 63% across countries and that 51% of mixed infections involve more than two strains. Furthermore, we estimate that 47% of symptomatic dual infections contain sibling strains likely to have been co-transmitted from a single mosquito, and find evidence of mixed infections propagated over successive infection cycles. Finally, leveraging data from the Malaria Atlas Project, we find that prevalence correlates within Africa, but not Asia, with both the rate of mixed infection and the level of IBD.

Coastal land is being lost worldwide at an alarming rate due to relative sea-level rise (RSLR) resulting from vertical land motion (VLM). This problem is understudied at a global scale, due to high spatial variability and difficulties reconciling VLM between regions. Here we provide self-consistent, high spatial resolution VLM observations derived from Interferometric Synthetic Aperture Radar for the 51 largest coastal cities, representing 22% of the global urban population. We show that peak subsidence rates are faster than current global mean sea-level rise rates and VLM contributions to RSLR are greater than IPCC projections in 90% and 53% of the cities respectively. Localized VLM worsens RSLR impacts on land and population in 73-75% of the cities, with Chittagong (Bangladesh), Yangon (Myanmar) and Jakarta (Indonesia) at greatest risk. With this dataset, accurate projections and comparisons of RSLR effects accounting for VLM are now possible for urban areas at a global scale. Sea-level rise resulting from climate change has rightly received substantial attention from researchers, practitioners and the public as an ongoing threat that needs to be addressed 1 . Yet lesser attention has been paid to land subsidence which can exceed tens of mm/year 2-4 , and increase local relative sea-level rise (RSLR) many times that of global mean sea-level rise of few mm/year alone 5,6 . Local RSLR, defined as sea-level rise relative to local land height, is what effectively matters for any coastal community. Furthermore, many coastal areas experiencing the fastest land subsidence are major cities built on flat, low elevation river deltas, exposing large populations and substantial economic value to the impacts of local RSLR 7,8 . Consequently, it is crucial to consider land subsidence when assessing coastal risks of RSLR 9,10 . Vertical land motion (VLM) -either subsidence (downward land motion) or uplift (upward land motion) -can be caused by several factors such as tectonics 11,12 , natural compaction of sediments 7,8 , groundwater, oil, and gas extraction 2,4 , reduced aggradation due to dams, levees, and loss of coastal vegetation 5 , and glacial isostatic adjustment 13,14 . Because these contributing factors vary significantly over a range of temporal and spatial scales, the contribution of VLM to RSLR has been difficult to assess on a global scale 15 . Many local-to regional-scale studies have mapped VLM at different coastal localities over different time

Spatio-temporal modelling is an increasingly popular topic in Statistics. Our paper contributes to this line of research by developing the theory, simulation and inference for a spatiotemporal Ornstein-Uhlenbeck process. We conduct detailed simulation studies and demonstrate the practical relevance of these processes in an empirical study of radiation anomaly data. Finally, we describe how predictions can be carried out in the Gaussian setting.A s .x/ A t .x/, 8 s < t; and A t .x/ \ .X .t; 1// D ;:(2)This implies that A t .x/ has a temporal component of . 1; t just like the classical case. These conditions on the integrating set also give the OU^process several characteristic properties Scand J Statist 44 Spatio-temporal OU processes 47 of the OU process: stationarity, Markovianity and an exponentially decaying autocorrelation function (ACF).In the literature, there are other definitions of spatio-temporal OU processes. When the driving Lévy noise is restricted to be Gaussian and X D R, a spatio-temporal process can be formed from the product of a temporal OU process with a spatial one (Traulsen et al., 2004). This is equal to a temporal OU process when the spatial component is fixed and a spatial OU process when the temporal component is fixed. Although this model features exponentially decaying temporal and spatial autocorrelation, one limitation is that the spatio-temporal autocorrelation is separable.Alternatively, if we discretise space, we can create a spatio-temporal OU process by considering a multivariate OU process where each vector component of the process corresponds to a spatial location. Such an approach was taken by Brix & Diggle (2001). Spatial information is contained in the covariance matrix of the driving Brownian motion. Although this also results in a separable spatio-temporal autocorrelation, the spatial autocorrelation is comparatively flexible to that obtained by the previous method.A further step in this direction would be to discretize the time domain. In this case, the OU process can be represented as an autoregressive model. A three-stage iterative procedure for the space-time modelling of autoregressive moving averages (ARMA) models was developed by Pfeifer & Deutrch (1980). The spatio-temporal autocorrelation in this case is defined differently and involves spatial neighbours of different 'orders'.The OU^process in (1) has four key advantages over these alternative models. Firstly, it accommodates non-Gaussian driving noise, which may be more appropriate in practice. Secondly, being a model in continuous time and space, it allows us to work with data of varying temporal and spatial scales. Thirdly, it allows for non-separable autocorrelation structures defined in the usual way. Finally, its integration set helps us identify the influence region for a particular time and space location. This could help scientists better understand the spatio-temporal interactions in real-life phenomena.A wide class of OU^processes for X D R, which we shall refer to as the g-class, is given bywhere ...